Acoustic emission (AE) is one of many available technologies for condition
health monitoring and diagnosis of rotating machines such as bearings. In
recent years there have been many developments in the use of Acoustic
Emission technology (AET) and its analysis for monitoring the condition of
rotating machinery whilst in operation, particularly on high speed machinery.
Unlike conventional technologies such as oil analysis, motor current signature
analysis (MCSA) and vibration analysis, AET has been introduced due to its
increased sensitivity in detecting the earliest stages of loss of mechanical
integrity.
This research presents an experimental investigation that is aimed at
developing a mathematical model and experimentally validating the influence of
operational variables such as film thickness, rotational speed, load, power loss,
and shear stress for variations of load and speed conditions, on generation of
acoustic emission in a hydrodynamic bearing. It is concluded that the power
losses of the bearing are directly correlated with acoustic emission levels. With
exponential law, an equation is proposed to predict power losses with
reasonable accuracy from an AE signal.
This experimental investigation conducted a comparative study between AE
and Vibration to diagnose the rubbing at high rotational speeds in the
hydrodynamic bearing. As it is the first known attempt in rotating machines. It
has been concluded, that AE parameters such as amplitude, can perform as a
reliable and sensitive tool for the early detection of rubbing between surfaces of
a hydrodynamic bearing and high speed shaft.
The application of vibration (PeakVue) analysis was introduced and compared
with demodulation. The results observed from the demodulation and PeakVue
techniques were similar in the rubbing simulation test. In fact, some defects on
hydrodynamic bearings would not have been seen in a timely manner without
the PeakVue analysis.In addition, the application of advanced signal processing and statistical
methods was established to extract useful diagnostic features from the acquired
AE signals in both time and frequency domain. It was also concluded that the
use of different signal processing methods is often necessary to achieve
meaningful diagnostic information from the signals. The outcome would largely
contribute to the development of effective intelligent condition monitoring
systems which can significantly reduce the cost of plant maintenance.
To implement these main objectives, the Sutton test rig was modified to assess
the capability of AET and vibration analysis as an effective tool for the detection
of incipient defects within high speed machine components (e.g. shafts and
hydrodynamic bearings).
The first chapter of this thesis is an introduction to this research and briefly
explains motivation and the theoretical background supporting this research.
The second and third chapters, summarise the relevant literature to establish
the current level of knowledge of hydrodynamic bearings and acoustic emission,
respectively. Chapter 4 describes methodologies and the experimental
arrangements utilized for this investigation. Chapter 5 discusses different NDT
diagnosis. Chapter 6 reports on an experimental investigation applied to
validate the relationship between AET on operational rotating machines, such
as film thickness, speed, load, power loss, and shear stress. Chapter 7 details
an investigation which compares the applicability of AE and vibration
technologies in monitoring a rubbing simulation on a hydrodynamic bearing.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/7888 |
| Date | 06 1900 |
| Creators | Mirhadizadeh, S. A. |
| Contributors | Mba, David |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University 2012. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner. |
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